When an individual visits a hospital, clinic, or physician, numerous patient records related to the visit are created. Patient records traditionally have been maintained in paper form by the provider responsible for creating the records, but more recently patient records are migrating to various electronic forms. As a result, significant efforts have been placed on creating a national/regional network for access to patient records, regardless of the location of the records, or the specific healthcare provider creating the records.
Numerous problems exist in developing a national/regional network for access to patient records. One problem is that there are hundreds of thousands of medical service providers (hospitals, clinics, doctor's offices, etc. . . . ) that operate more or less autonomously. As a result there is considerable inconsistency with respect to the software and equipment used to capture and store the data, the fields of data captured, the formats used, archiving policies, and so forth. A second problem is that each provider wants ultimate control over its own data, but wants easy access to data generated by others. Still a third problem is that patients want to keep their records confidential.
Several different systems have been proposed. A fully centralized approach is known where the data records are stored in a central record repository. (See e.g., US 2006/0129434 to Smitherman, US 2007/0055552 to St. Clair, US 2007/0016450 to Bhora). However, this model requires broad agreement among healthcare providers that all data records will be managed by a central authority. Moreover, the scaling, reliability, and privacy issues associated with a central record repository are formidable.
A fully decentralized approach is also known, which allows a provider to access records distributed across multiple healthcare database management systems as though they were stored locally at the provider. Using this approach, electronic records regarding a given patient can be assembled on demand to provide a complete healthcare history of the individual. For example, Connecting For Health consortium (see http://www.connecting-forhealth.org/assets/reports/linking_report—2—2005.pdf), provides a central store that only maintains: (a) name, address, age, gender and other non-unique patient identification information; and (b) links to records in the local databases. (See also e.g., US 2005/0246205 to Wang). A drawback to using a decentralized approach is that users can receive incomplete patient records because of the diverse set of data sources, and the inability to extract relevant information from some of the sources. Moreover, many providers can be unwilling or unable to manage a local infrastructure.
US 2005/0027995 to Menschik teaches a decentralized network for mediating peer-to-peer transfer of patent medical data including a plurality of decentralized agents associated with a health care provider and connected to a central network. Unfortunately, Menschik requires all peers to authenticate all other peers, which results in an unwieldy, distributed mesh of trust. Such an approach lacks scalability because the number of authenticated relationships increases on the order of N2 where N is the number of peers in the system. The system becomes impractical for N anywhere near as large as that required for a national medical network.
In between the centralized and decentralized approaches are hybrid approaches where some patient records can be centralized and others are stored in a decentralized network. (See e.g., US 2007/0214016 to Bennett). There are several new problems with these hybrid approaches. For example, problems can arise in deciding whether to store a particular record in a centralized or local data store. In addition, the relative importance of a particular electronic healthcare record can change with the occurrence of contemporaneous events, and therefore the record might not be placed properly in the right data store.
Currently, there is no solution that resolves all of the problems. Consequently, there is still a need for a system that combines demographic, clinical, and other practice-related data from multiple independent data sources, that can be conveniently mined and scaled easily as new data sources are added to the system.